A general identification condition for causal effects
Year: 2002
Type: article
Abstract: This paper concerns the assessment of the effects of actions or policy interventions from a combination of: (i) nonexperimental data, and (ii) substantive assumptions. The assumptions are encoded in the form of a directed acyclic graph, also called “causal graph”, in which some variables are presumed to be unobserved. The paper establishes a necessary and sufficient criterion for the identifiability of the causal effects of a singleton variable on all other variables in the model, and apowerful sufficient criterion for the effects of a singleton variable on any set of variables.
Authors: Jin Tian, Judea Pearl
Institution: University of California, Los Angeles
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1992816730